Chair of Digital Health

Imagine personalised health assistance that is omnipresent and continuously available to everyone while being intelligible – we are working to realise this vision.

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Clinical decision support tools play a pivotal role in bridging the gap between research findings and their application in clinical practice. The primary goals of building a functional prototype for a web-based clinical decision support tool are to validate the concept, gather user feedback for refinement, demonstrate feasibility, and identify and address technical and usability challenges early in development.

Background ACR introduces and discusses current scientific topics in context recognition (i.e. identifying and describing a situation or state of the environment in the computer) from sensor data, including time series data and image data. Each term, new projects and medical applications are off...

Background It is considered impossible to know the maximum possible learning performance of a machine learning (ML) algorithm on a given dataset before running the algorithm. The performance of an ML algorithm depends on many factors, such as the quality and size of the data, the complexity of t...

Background It is considered impossible to know the maximum possible learning performance of a machine learning (ML) algorithm on a given dataset before running the algorithm. The performance of an ML algorithm depends on many factors, such as the quality and size of the data, the complexity of t...

Vagus nerve stimulation has emerged as a promising nonpharmacological intervention for a range of medical conditions. Invasive vagus nerve stimulation via implanted stimulators is employed as a noninvasive treatment for drug-resistant epilepsy, congestive heart failure, and major depression. Noninvasive application of electrical stimulation offers advantages such as reduced infection risk and enhanced usability. We aim to investigate the mechanisms underlying non-invasive so-called transcutaneous Vagus Nerve Stimulation (tVNS) and optimize its clinical application using computational modeling.

Friedrich-Alexander-Universität Erlangen-Nürnberg